Infrared Small Target Detection Based on Tensor Tree Decomposition and Self-Adaptive Local Prior

Infrared small target detection plays a crucial role in both military and civilian systems. However, current detection methods face significant challenges in complex scenes, such as inaccurate background estimation, inability to distinguish targets from similar non-target points, and poor robustness...
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Autor*in:	Guiyu Zhang [verfasserIn] Zhenyu Ding [verfasserIn] Qunbo Lv [verfasserIn] Baoyu Zhu [verfasserIn] Wenjian Zhang [verfasserIn] Jiaao Li [verfasserIn] Zheng Tan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	infrared small target detection tensor tree decomposition self-adaptive local prior spatial–temporal total variation

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 16(2024), 6, p 1108
Übergeordnetes Werk:	volume:16 ; year:2024 ; number:6, p 1108

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs16061108

Katalog-ID:	DOAJ099818736

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spelling	10.3390/rs16061108 doi (DE-627)DOAJ099818736 (DE-599)DOAJe3d2e1895554442895e23202333a3e61 DE-627 ger DE-627 rakwb eng Guiyu Zhang verfasserin aut Infrared Small Target Detection Based on Tensor Tree Decomposition and Self-Adaptive Local Prior 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Infrared small target detection plays a crucial role in both military and civilian systems. However, current detection methods face significant challenges in complex scenes, such as inaccurate background estimation, inability to distinguish targets from similar non-target points, and poor robustness across various scenes. To address these issues, this study presents a novel spatial–temporal tensor model for infrared small target detection. In our method, we introduce the tensor tree rank to capture global structure in a more balanced strategy, which helps achieve more accurate background estimation. Meanwhile, we design a novel self-adaptive local prior weight by evaluating the level of clutter and noise content in the image. It mitigates the imbalance between target enhancement and background suppression. Then, the spatial–temporal total variation (STTV) is used as a joint regularization term to help better remove noise and obtain better detection performance. Finally, the proposed model is efficiently solved by the alternating direction multiplier method (ADMM). Extensive experiments demonstrate that our method achieves superior detection performance when compared with other state-of-the-art methods in terms of target enhancement, background suppression, and robustness across various complex scenes. Furthermore, we conduct an ablation study to validate the effectiveness of each module in the proposed model. infrared small target detection tensor tree decomposition self-adaptive local prior spatial–temporal total variation Science Q Zhenyu Ding verfasserin aut Qunbo Lv verfasserin aut Baoyu Zhu verfasserin aut Wenjian Zhang verfasserin aut Jiaao Li verfasserin aut Zheng Tan verfasserin aut In Remote Sensing MDPI AG, 2009 16(2024), 6, p 1108 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:16 year:2024 number:6, p 1108 https://doi.org/10.3390/rs16061108 kostenfrei https://doaj.org/article/e3d2e1895554442895e23202333a3e61 kostenfrei https://www.mdpi.com/2072-4292/16/6/1108 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 16 2024 6, p 1108
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allfieldsSound	10.3390/rs16061108 doi (DE-627)DOAJ099818736 (DE-599)DOAJe3d2e1895554442895e23202333a3e61 DE-627 ger DE-627 rakwb eng Guiyu Zhang verfasserin aut Infrared Small Target Detection Based on Tensor Tree Decomposition and Self-Adaptive Local Prior 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Infrared small target detection plays a crucial role in both military and civilian systems. However, current detection methods face significant challenges in complex scenes, such as inaccurate background estimation, inability to distinguish targets from similar non-target points, and poor robustness across various scenes. To address these issues, this study presents a novel spatial–temporal tensor model for infrared small target detection. In our method, we introduce the tensor tree rank to capture global structure in a more balanced strategy, which helps achieve more accurate background estimation. Meanwhile, we design a novel self-adaptive local prior weight by evaluating the level of clutter and noise content in the image. It mitigates the imbalance between target enhancement and background suppression. Then, the spatial–temporal total variation (STTV) is used as a joint regularization term to help better remove noise and obtain better detection performance. Finally, the proposed model is efficiently solved by the alternating direction multiplier method (ADMM). Extensive experiments demonstrate that our method achieves superior detection performance when compared with other state-of-the-art methods in terms of target enhancement, background suppression, and robustness across various complex scenes. Furthermore, we conduct an ablation study to validate the effectiveness of each module in the proposed model. infrared small target detection tensor tree decomposition self-adaptive local prior spatial–temporal total variation Science Q Zhenyu Ding verfasserin aut Qunbo Lv verfasserin aut Baoyu Zhu verfasserin aut Wenjian Zhang verfasserin aut Jiaao Li verfasserin aut Zheng Tan verfasserin aut In Remote Sensing MDPI AG, 2009 16(2024), 6, p 1108 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:16 year:2024 number:6, p 1108 https://doi.org/10.3390/rs16061108 kostenfrei https://doaj.org/article/e3d2e1895554442895e23202333a3e61 kostenfrei https://www.mdpi.com/2072-4292/16/6/1108 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 16 2024 6, p 1108
language	English
source	In Remote Sensing 16(2024), 6, p 1108 volume:16 year:2024 number:6, p 1108
sourceStr	In Remote Sensing 16(2024), 6, p 1108 volume:16 year:2024 number:6, p 1108
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institution	findex.gbv.de
topic_facet	infrared small target detection tensor tree decomposition self-adaptive local prior spatial–temporal total variation Science Q
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container_title	Remote Sensing
authorswithroles_txt_mv	Guiyu Zhang @@aut@@ Zhenyu Ding @@aut@@ Qunbo Lv @@aut@@ Baoyu Zhu @@aut@@ Wenjian Zhang @@aut@@ Jiaao Li @@aut@@ Zheng Tan @@aut@@
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author	Guiyu Zhang
spellingShingle	Guiyu Zhang misc infrared small target detection misc tensor tree decomposition misc self-adaptive local prior misc spatial–temporal total variation misc Science misc Q Infrared Small Target Detection Based on Tensor Tree Decomposition and Self-Adaptive Local Prior
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topic_title	Infrared Small Target Detection Based on Tensor Tree Decomposition and Self-Adaptive Local Prior infrared small target detection tensor tree decomposition self-adaptive local prior spatial–temporal total variation
topic	misc infrared small target detection misc tensor tree decomposition misc self-adaptive local prior misc spatial–temporal total variation misc Science misc Q
topic_unstemmed	misc infrared small target detection misc tensor tree decomposition misc self-adaptive local prior misc spatial–temporal total variation misc Science misc Q
topic_browse	misc infrared small target detection misc tensor tree decomposition misc self-adaptive local prior misc spatial–temporal total variation misc Science misc Q
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title	Infrared Small Target Detection Based on Tensor Tree Decomposition and Self-Adaptive Local Prior
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title_full	Infrared Small Target Detection Based on Tensor Tree Decomposition and Self-Adaptive Local Prior
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author_browse	Guiyu Zhang Zhenyu Ding Qunbo Lv Baoyu Zhu Wenjian Zhang Jiaao Li Zheng Tan
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title_sort	infrared small target detection based on tensor tree decomposition and self-adaptive local prior
title_auth	Infrared Small Target Detection Based on Tensor Tree Decomposition and Self-Adaptive Local Prior
abstract	Infrared small target detection plays a crucial role in both military and civilian systems. However, current detection methods face significant challenges in complex scenes, such as inaccurate background estimation, inability to distinguish targets from similar non-target points, and poor robustness across various scenes. To address these issues, this study presents a novel spatial–temporal tensor model for infrared small target detection. In our method, we introduce the tensor tree rank to capture global structure in a more balanced strategy, which helps achieve more accurate background estimation. Meanwhile, we design a novel self-adaptive local prior weight by evaluating the level of clutter and noise content in the image. It mitigates the imbalance between target enhancement and background suppression. Then, the spatial–temporal total variation (STTV) is used as a joint regularization term to help better remove noise and obtain better detection performance. Finally, the proposed model is efficiently solved by the alternating direction multiplier method (ADMM). Extensive experiments demonstrate that our method achieves superior detection performance when compared with other state-of-the-art methods in terms of target enhancement, background suppression, and robustness across various complex scenes. Furthermore, we conduct an ablation study to validate the effectiveness of each module in the proposed model.
abstractGer	Infrared small target detection plays a crucial role in both military and civilian systems. However, current detection methods face significant challenges in complex scenes, such as inaccurate background estimation, inability to distinguish targets from similar non-target points, and poor robustness across various scenes. To address these issues, this study presents a novel spatial–temporal tensor model for infrared small target detection. In our method, we introduce the tensor tree rank to capture global structure in a more balanced strategy, which helps achieve more accurate background estimation. Meanwhile, we design a novel self-adaptive local prior weight by evaluating the level of clutter and noise content in the image. It mitigates the imbalance between target enhancement and background suppression. Then, the spatial–temporal total variation (STTV) is used as a joint regularization term to help better remove noise and obtain better detection performance. Finally, the proposed model is efficiently solved by the alternating direction multiplier method (ADMM). Extensive experiments demonstrate that our method achieves superior detection performance when compared with other state-of-the-art methods in terms of target enhancement, background suppression, and robustness across various complex scenes. Furthermore, we conduct an ablation study to validate the effectiveness of each module in the proposed model.
abstract_unstemmed	Infrared small target detection plays a crucial role in both military and civilian systems. However, current detection methods face significant challenges in complex scenes, such as inaccurate background estimation, inability to distinguish targets from similar non-target points, and poor robustness across various scenes. To address these issues, this study presents a novel spatial–temporal tensor model for infrared small target detection. In our method, we introduce the tensor tree rank to capture global structure in a more balanced strategy, which helps achieve more accurate background estimation. Meanwhile, we design a novel self-adaptive local prior weight by evaluating the level of clutter and noise content in the image. It mitigates the imbalance between target enhancement and background suppression. Then, the spatial–temporal total variation (STTV) is used as a joint regularization term to help better remove noise and obtain better detection performance. Finally, the proposed model is efficiently solved by the alternating direction multiplier method (ADMM). Extensive experiments demonstrate that our method achieves superior detection performance when compared with other state-of-the-art methods in terms of target enhancement, background suppression, and robustness across various complex scenes. Furthermore, we conduct an ablation study to validate the effectiveness of each module in the proposed model.
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title_short	Infrared Small Target Detection Based on Tensor Tree Decomposition and Self-Adaptive Local Prior
url	https://doi.org/10.3390/rs16061108 https://doaj.org/article/e3d2e1895554442895e23202333a3e61 https://www.mdpi.com/2072-4292/16/6/1108 https://doaj.org/toc/2072-4292
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author2	Zhenyu Ding Qunbo Lv Baoyu Zhu Wenjian Zhang Jiaao Li Zheng Tan
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doi_str	10.3390/rs16061108
up_date	2024-07-04T00:28:10.463Z
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Infrared Small Target Detection Based on Tensor Tree Decomposition and Self-Adaptive Local Prior

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